65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""CIFAR100 small images classification dataset.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import numpy as np
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras.datasets.cifar import load_batch
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from tensorflow.python.keras.utils.data_utils import get_file
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('keras.datasets.cifar100.load_data')
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def load_data(label_mode='fine'):
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"""Loads CIFAR100 dataset.
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Arguments:
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label_mode: one of "fine", "coarse".
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Returns:
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Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
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Raises:
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ValueError: in case of invalid `label_mode`.
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"""
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if label_mode not in ['fine', 'coarse']:
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raise ValueError('`label_mode` must be one of `"fine"`, `"coarse"`.')
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dirname = 'cifar-100-python'
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origin = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
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path = get_file(dirname, origin=origin, untar=True)
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fpath = os.path.join(path, 'train')
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x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')
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fpath = os.path.join(path, 'test')
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x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')
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y_train = np.reshape(y_train, (len(y_train), 1))
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y_test = np.reshape(y_test, (len(y_test), 1))
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if K.image_data_format() == 'channels_last':
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x_train = x_train.transpose(0, 2, 3, 1)
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x_test = x_test.transpose(0, 2, 3, 1)
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return (x_train, y_train), (x_test, y_test)
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